geodesic parameter

geodesic parameter的基本解释

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测地参数

与geodesic parameter相关的网络例句 [注：此内容来源于网络，仅供参考]

First make the subjects give either right or wrong responses to the same question with different b value. When estimating the abilities of the subjects with the use of one-parameter or two-parameter Logistic model, it is found that there exists two kinds of unfits.(2) Estimate the abilities of the subjects after introducing c parameter on the basis of the two-parameter model. The first unfit can be rectified. However, the second unfit still exists and the third unfit appears.(3) Then estimate again after introducing y parameter. It is discovered that the second unfit is rectified, but the first unfit still exists and the fourth unfit appears.(4) Form Logistic four-parameter model by introducing c parameter and y parameter at the same time and estimate one more time. This model makes all kinds of unfits, including the first, second, third and fourth unfits, rectified.

1设计这批被试分别做对或做错一道b值不同的试题，用Logistic单、双参数模型对被试进行能力估计时，发现被试能力估计存在着两类失拟现象；(2)在双参数模型基础上增加c参数，对被试进行能力估计，发现c参数能有效纠正第一失拟现象，然而仍然存在第二失拟现象，同时还存在第三失拟现象；(3)在双参数模型基础上增加γ参数，再对被试进行能力估计，发现γ参数能有效纠正第二失拟现象，而仍然存在第一失拟现象，同时还存在第四失拟现象；(4)同时增加c、γ参数形成Logistic四参数模型，再对被试进行能力估计，这时该模型对各类失拟现象，包括第一、第二、第三、第四失拟现象都具有良好拟合能力。

Isomap is one of the representative techniques of nonlinear dimensionality reduction.It extends classical multidimensional scaling by considering approximate geodesic distance. However,Isomap is sensitive to noise because the approximate geodesic distance is constructed on the basis of Euclidean distance. In this paper,a kernel-induced distance metric defined in the feature space is introduced instead of the Euclidean distance to evaluate the geodesic distance and construct the corresponding neighborhood graph.

采用核方法在特征空间推导出一类异于欧氏距离的新度量，代替等度规特征映射中的对噪声敏感的欧式距离，用新度量构造测地距离和相应的最小近邻图，提高Isomap算法的抗噪声能力。

A generalized Gaussian Laplacian eigenmap algorithm based on geodesic distance is proposed,which incorporates geodesic distance and generalized Gaussian function into the original Laplacian eigenmap algorithm.GGLE algorithm can adjust the similarities between nodes of neighborhood graph,and can preserve the different degrees of local properties by using super-Gaussian function,Gaussian function or sub-Gaussian function.Moreover,GGLE can avoid the deficiency of Euclidean distance by using geodesic distance when neighborhoods of data points are enlarged for preserving more neighborhood relations.Experimental results show that the global low-dimensional coordinates obtained by GGLE have different clustering properties and different degrees of preserving local neighborhood structures when different generalized Gaussian functions are used to measuring the similarities between high-dimensional data points.(3) An ensemble-based discriminant algorithm based on GGLE is proposed.

该算法将测地线距离和广义高斯函数融合到传统的拉普拉斯特征映射算法中，可以调整近邻图结点间的相似度，通过选择超高斯、高斯或者次高斯函数来实现不同程度的近邻局部特性的保持；而且当需要保持更多的近邻关系使得数据点邻域增大时，采用测地线距离可以避免欧氏距离度量不合理的缺陷；实验结果表明在用不同的广义高斯函数度量高维数据点间的相似度时，局部近邻结构保持的程度是不同的，GGLE获得的全局低维坐标也呈现出不同的聚类特性。

与geodesic parameter相关的网络解释 [注：此内容来源于网络，仅供参考]

geodesic parameter：测地参数

geodesic line 测地线 | geodesic parameter 测地参数 | geodesic torsion 测地挠率